Yang Cheng-Hong, Moi Sin-Hua, Chuang Li-Yeh, Yuan Shyng-Shiou F, Hou Ming-Feng, Lee Yi-Chen, Chang Hsueh-Wei
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan.
Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan.
Biomed Res Int. 2017;2017:2563910. doi: 10.1155/2017/2563910. Epub 2017 Jan 4.
The interaction between the meiotic recombination 11 homolog A (MRE11) oncoprotein and breast cancer recurrence status remains unclear. The aim of this study was to assess the interaction between MRE11 and clinicopathologic variables in breast cancer. A dataset for 254 subjects with breast cancer (220 nonrecurrent and 34 recurrent) was used in individual and cumulated receiver operating characteristic (ROC) analyses of MRE11 and 12 clinicopathologic variables for predicting breast cancer recurrence. In individual ROC analysis, the area under curve (AUC) for each predictor of breast cancer recurrence was smaller than 0.7. In cumulated ROC analysis, however, the AUC value for each predictor improved. Ten relevant variables in breast cancer recurrence were used to find the optimal prognostic indicators. The presence of any six of the following ten variables had a high (79%) sensitivity and a high (70%) specificity for predicting breast cancer recurrence: tumor size ≥ 2.4 cm, tumor stage II/III, therapy other than hormone therapy, age ≥ 52 years, MRE11 positive cells > 50%, body mass index ≥ 24, lymph node metastasis, positivity for progesterone receptor, positivity for epidermal growth factor receptor, and negativity for estrogen receptor. In conclusion, this study revealed that these 10 clinicopathologic variables are the minimum discriminators needed for optimal discriminant effectiveness in predicting breast cancer recurrence.
减数分裂重组11同源物A（MRE11）癌蛋白与乳腺癌复发状态之间的相互作用仍不清楚。本研究的目的是评估MRE11与乳腺癌临床病理变量之间的相互作用。在对MRE11和12个临床病理变量进行的个体和累积受试者工作特征（ROC）分析中，使用了一个包含254例乳腺癌患者（220例未复发和34例复发）的数据集来预测乳腺癌复发。在个体ROC分析中，每个乳腺癌复发预测指标的曲线下面积（AUC）均小于0.7。然而，在累积ROC分析中，每个预测指标的AUC值有所提高。利用乳腺癌复发的10个相关变量来寻找最佳预后指标。以下10个变量中任意6个变量的存在对预测乳腺癌复发具有高（79%）敏感性和高（70%）特异性：肿瘤大小≥2.4 cm、肿瘤分期II/III、非激素治疗、年龄≥52岁、MRE11阳性细胞>50%、体重指数≥24、淋巴结转移、孕激素受体阳性、表皮生长因子受体阳性和雌激素受体阴性。总之，本研究表明，这10个临床病理变量是预测乳腺癌复发时实现最佳判别效能所需的最小判别因素。
